Multicast technology has emerged as a viable and popular approach for supporting extended wireless communication services. For example, Internet Protocol (IP) multicast has been adopted as a technique for many-to-many communication over an IP infrastructure. It is expected that such services can be offered with different service level agreements (SLAs). Therefore, performance monitoring is important to maintain these different SLAs.
Traditional monitoring systems treats the network(s) between a source and a sink as one entity. Under this model, the system is capable of evaluating the performance as experienced between the measurement points, but has no visibility into the internal event of the network. As such, performance metrics can be readily correlated to the events. Also, with conventional approaches, extending performance evaluation sessions to a new segment requires adding a new instance of source and/or sink at the new point. Such configuration is characterized by a considerable overhead, and cost as well as presents scalability issues that may limit deployment. It is further recognized that traditional systems are not able to differentiate service degradation events resulting from network problems from those caused by mobility related events.
Therefore, there is a need for an approach for monitoring performance metrics in a multicast environment, while enhancing network visibility and scalability.